Data compiled in the prepDataForModels.R script
Here are the climate variables we could potentially use in the models
| variable | unit |
|---|---|
| swe_meanAnnAvg_30yr | mm |
| tmean_meanAnnAvg_30yr | degrees C |
| prcp_meanAnnTotal_30yr | mm |
| precip_Seasonality_meanAnnAvg_30yr | coef. of variation |
| PrecipTempCorr_meanAnnAvg_30yr | correlation coef. |
| isothermality_meanAnnAvg_30yr | coef. of variation |
| annWaterDeficit_meanAnnAvg_30yr | mm of water/degrees celsius |
| annWetDegDays_meanAnnAvg_30yr | degree days |
| annVPD_mean_meanAnnAvg_30yr | KPa |
This is using a subset of the data (50,000 rows), just for runtime purposes
Below is the correlation between only climate predictors that are averaged across 30 years * Dropped tmin, tmax, t_warmest month, and t_coldest month and replaced w/ MAT * Drop prcp wettest month - use MAP only * dropped precip of driest month–was highly correlated w/ precip and precip seasonality * drop prcpSeasonality – highly correlated w/ water deficit and wet degree days * Replace VPDmean with VPDmax * Probably drop FreezeMon ** also dropped vp (which we didn’t talk about, but is highly correlated w/ VPD and pretty highly correlated w/ precip)
spatial distribution of VPD_max
spatial distribution of Wet degree days
spatial distribution of water deficit